import requests
import datetime
import pandas as pd
import pymysql
from sklearn.preprocessing import MinMaxScaler
from joblib import dump

class WeatherUtils(object):
    """天气类
    Args:
        object (_type_): _description_
    """

def __init__(self):
    self.date_list = [
        '2024-07-01',
        '2024-08-01',
        '2024-09-01',
        '2024-10-01',
        '2024-11-01',
        '2024-12-01',
    ]
    self.url = 'http://v1.yiketianqi.com/api'

def get_data(self):
    """获取天气预报
    """
    data_list = []
    for d in self.data_list:
        conf =  {
            'appid': '21611415',# 使用自己注册的id
            'appseceret': 'gcrwu5k9',
            'version': 'history',
            'year': d[:4],
            'month': d[5:7],
            'city': '南昌'
        }

        # 发起请求获取数据
        res = requests.get(self.url + '?', params=conf)
        res_data = res.json()
        #print(res_data)
        #break
        for i in res_data['data']:
            data_list.append({
                'data': datetime.datetime.strptime(i['ymd'], '%Y-%m-%d'),
                'bWendu': i['bWendu'],
                'yWendu': i['yWendu'],
                'tianqi': i['tianqi'],
                'fengli': i['fengli'],
            })
        df = pd.DataFrame(data_list)
        df.to_csv('gitee/timing/wether.csv')

class MysqlUtils(object):

    def __init__(self):
        self.conn = pymysql.connect(
            host= '127.0.0.1',
            user='root',
            password='root',
            database='scenic',
            port= 3306,         # 明确指定端口
            charset='utf8mb4'   # 添加字符集设置
        )
        self.weather_data = pd.read_csv('./timing/weather.csv')

    def is_holiday(self, date):
        """是否节假日判断
        """
        if date in ['2024-09-03','2024-10-01','2024-10-02','2024-10-03','2024-10-04','2024-10-05','2024-10-06','2024-10-07','2025-01-01','2025-01-02','2025-01-03']:
            return 1
        return 0
    
    def get_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
        SELECT DATE(g.create_time) as date, count(*) as count FROM order_user_gate_rel g WHERE DATE(g.create_time) < '2025-01-01' GROUP BY date
        """
        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)

        # 合并天气数据
        self.weather_data['data'] = pd.to_datetime(self.weather_data['date'])
        df['date'] = pd.to_datetime(df['date'])
        df_pivot = pd.merge(self.weather_data, df, on='date')
        print(df_pivot.head)
        df_pivot.set_index('date', inplace=True)

        df_pivot['dow'] = df_pivot.index.dayofweek # 星期几(0-6)
        df_pivot['month'] = df_pivot.index.month # 月份
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        # print(df_pivot.head)

        # 对星期几和月份进行独热编码
        df_pivot = pd.get_dummies(df_pivot, columns=['dow','month', 'tianqi', 'fengli'], dtype=int)
        # 对温度进行类型转换
        df_pivot['bWendu'] = df_pivot['bWendu'].str.replace('°','').astype(int)
        df_pivot['yWendu'] = df_pivot['yWendu'].str.replace('°','').astype(int)

        # 归一化入园数
        scaler = MinMaxScaler()
        features = df_pivot[['count']]
        df_pivot['count'] = scaler.fit_transform(features)
        # 归一化天气
        weather_features = df_pivot[['bWendu','yWendu']]
        dump(scaler, 'timing/scaler.joblib')
        df_pivot[['bWendu','yWendu']] = scaler.fit_transform(weather_features)
        dump(weather_features, 'timing/weather_features.joblib')

        df_pivot.to_csv('gitee/timing/scenic_data.csv', index=False)

if __name__ == '__main__':
    # wu = WeatherUtils()
    # wu.get_data()
    mu = MysqlUtils()
    mu.get_data()